Brain tumor segmentation and classification using optimized U-Net

This paper presents an optimization-driven classifier for classifying the brain tumour considering MRI. Here, the pre-operative and post-operative MRI is subjected to pre-processing, which is performed using filtering and Region of Interest (RoI) extraction techniques. The pre-processed output is fed to segmentation wherein the U-Net model is adapted for generating the segments. Then, the extraction of histogram features is done and the classification of tumours is done by U-Net, which is trained using the proposed Poor Bird Swarm Optimization algorithm (PRBSA). Here, PRBSA is the integration of the Poor and rich optimization (PRO) algorithm and Bird Swarm Algorithm (BSA). At last, the classified output is considered for pixel change detection, which is carried out using speeded-up robust features (SURF). The proposed PRBSA-based U-Net offered improved performance with the highest accuracy of 94%, highest sensitivity of 93.7%, and highest specificity of 94%.